Em

Empirical Eye – Bringing AI to the Physical World

Hacker News

Empirical Eye – Bringing AI to the Physical World

Hey Hacker News, Our company has just released a sensor+computer module called the Empirical Eye. It's basically a stereo vision system + on-board embedded CPU+GPU + WiFi (plus some mounting straps). Internally there is also an IMU sensor, and the ability to attach others via GPIO pins. The Eye is meant to be easily attached to physical machines, to collect and stream data, and enable novel machine learning applications (and one day: control those machines!). The goal with this device is to bring ML/AI to the physical world. We've made our own "visual quality control" software with the device. We're doing a device giveaway to developers for feedback and troubleshooting, which will help us refine it. We'd love to send some out to the HN crowd (quantities limited to about 75), and get your feedback and see what you build! We want to make life easier for running ML experiments and developing new applications. The primary motivation has been the field of robotics, but we see it as a flexible module that can be attached to any number of devices or machines, so feel free to be creative :) We're looking to do another production run in a month or so, with a cleaner industrial design that'll look a bit more professional. Upon delivery you'll get the device's software API. It's a Linux OS not that different from a Raspberry Pi OS, and we have basic data collection + streaming scripts written in Python for your convenience. If you're interested, definitely check out the site and sign up for a free device. Let us know what you want to develop, and ideally a quick one-liner describing your background experience. www.empiricalautomation.com Happy to answer any questions, cheers :)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, computer, new · Missing: agents, macos, agent
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, hacker news, ide · Missing: https docs, excited, exist
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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